This study is part of a Phase II STTR project to develop an algorithm called CipherSensor to
apply feature extraction and machine learning techniques to non-invasive hemodynamic data to
identify early signs of acute blood loss. The availability of this information may help to
establish required interventions for treating trauma patients and battlefield casualties.
Study hypothesis: Hemodynamic changes measured non-invasively during the blood donation
process can be modeled to provide early estimations of blood loss.